A 3-step AI coding workflow for solo founders | Ryan Carson (5x founder)

Introduction

In this tutorial, we will explore a three-step AI coding workflow designed for solo founders, inspired by Ryan Carson's experience in building and scaling startups. This structured approach transforms chaotic AI coding into a reliable process, enabling founders to create products with minimal engineering support. Whether you are a solo entrepreneur or a product manager, this guide will equip you with the tools and methodologies to streamline your AI development.

Step 1: Establish a Three-File System

Creating a structured environment is crucial for effective AI coding. Follow these steps to set up your three-file system:

1. Create a Product Requirements Document (PRD)

   • Use AI tools like Cursor to generate the PRD.
   • Ensure it clearly outlines the product vision, features, and functionality.

2. Develop a Task List from the PRD

   • Break down the PRD into actionable tasks.
   • Utilize AI to assist in generating a comprehensive list that reflects the steps required to develop the product.

3. File Organization

   • Save the PRD and task list in a dedicated project folder.
   • Maintain a third file for documentation, which should include notes and changes throughout the development process.

Step 2: Systematic Feature Development

Once your files are organized, it's time to systematically build features using AI tools. Here’s how to proceed:

1. Feature Selection

   • Choose a feature from your task list to work on first.
   • Make sure you have adequate context about the feature to inform your AI tool effectively.

2. Utilize AI Coding Tools

   • Use tools like Cursor or Repo Prompt to generate code snippets for the selected feature.
   • Provide clear and specific prompts to ensure the AI understands the context and requirements.

3. Iterative Development

   • Test the generated code regularly to ensure it meets the desired specifications.
   • Make adjustments based on feedback from testing and integrate any necessary changes into your documentation.

Step 3: Leverage Model Context Protocols

Model Context Protocols (MCPs) can significantly enhance your AI's capabilities beyond simple coding. Follow these steps to implement MCPs:

1. Understanding MCPs

   • Familiarize yourself with the concept of MCPs, which provide structured context to AI models, improving their output quality.
   • Refer to resources like the MCP documentation from Anthropic for deeper insights.

2. Implementing MCPs

   • Use specific MCPs for tasks such as front-end testing and context management.
   • Experiment with different MCPs to find the best fit for your project needs.

3. Documentation and Feedback

   • Keep detailed records of how each MCP performs with your AI tools.
   • Use this information to refine your process and improve future iterations.

Conclusion

By following this three-step AI coding workflow, you can transform your approach to product development as a solo founder. Establishing a structured file system, systematically building features, and leveraging MCPs will not only streamline your process but also enhance the quality of your outputs. As you implement these steps, remember that providing clear context to your AI tools is key to speeding up your development. Start applying these principles today to maximize your productivity and reduce reliance on larger engineering teams.